{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "024fcad8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:56:34.133176Z",
     "start_time": "2024-06-20T02:56:31.300359Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version 3.1.1\n"
     ]
    }
   ],
   "source": [
    "import deepquantum as dq\n",
    "import numpy as np\n",
    "import torch\n",
    "print('version',dq.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c41d706",
   "metadata": {},
   "source": [
    "# 基于Fock 后端构建光量子线路"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44e594c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-29T11:44:44.581118Z",
     "start_time": "2023-11-29T11:44:44.578840Z"
    }
   },
   "source": [
    "## 基础"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17dd9d4c",
   "metadata": {},
   "source": [
    "a. 初始化线路需要的参数：\\\n",
    "nmode: 表示mode 数 \\\n",
    "init_state: 表示初态设置， 初态设置有两种，fock basis 输入 [1,0,1,0], 此时basis=True, 或者叠加态 [($\\frac{\\sqrt{2}}{2}$, [1,0,1,0]),($\\frac{\\sqrt2}{2}$, [0,1,1,0])] 此时basis=False \\\n",
    "cutoff: 每个mode上支持的最大光子数+1 \\\n",
    "b. 当前模拟器支持加入下列门 \\\n",
    "1. ps 门： ps参数表示相移器对应的相位角 $e^{i\\theta}$  \n",
    "2. bs门： bs有两个参数，对应的U矩阵如下\n",
    "$$\n",
    "\\begin{pmatrix}\n",
    "\\cos{\\theta}&-e^{-i\\phi}\\sin{\\theta}\\\\\n",
    "e^{i\\phi}sin{\\theta}&\\cos{\\theta}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "但是bs门一般默认只有一个参数, $bs_\\theta$和$bs_\\phi$.\\\n",
    "对于$bs_\\theta$($\\phi = \\pi/2$)， 当$\\theta$为$\\pi/4$ 时，对应的BS门为 \n",
    "$$\\frac{\\sqrt{2}}{2}\n",
    "\\begin{pmatrix}\n",
    "1&i\\\\\n",
    "i&1\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "对应的分束比为1:1 \\\n",
    "当$\\sin{\\theta} = \\sqrt{1/3}$, $\\theta=0.61547$ 时， 对应的分束比为1：2, 对应的BS门为:\n",
    "\n",
    "$$\\sqrt{\\frac{1}{3}}\n",
    "\\begin{pmatrix}\n",
    "\\sqrt{2}&i\\\\\n",
    "i&\\sqrt{2}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "3. H 门 \\\n",
    "$$\n",
    "\\begin{pmatrix}\n",
    "\\cos{\\frac{\\theta}{2}}&\\sin{\\frac{\\theta}{2}}\\\\\n",
    "\\sin{\\frac{\\theta}{2}}&-\\cos{\\frac{\\theta}{2}}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "4. Rx 门 \\\n",
    "$$\n",
    "\\begin{pmatrix}\n",
    "\\cos{\\frac{\\theta}{2}}&i\\sin{\\frac{\\theta}{2}}\\\\\n",
    "i\\sin{\\frac{\\theta}{2}}&\\cos{\\frac{\\theta}{2}}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "5. Ry 门\n",
    "$$\n",
    "\\begin{pmatrix}\n",
    "\\cos{\\frac{\\theta}{2}}&-\\sin{\\frac{\\theta}{2}}\\\\\n",
    "\\sin{\\frac{\\theta}{2}}&\\cos{\\frac{\\theta}{2}}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "6. 50：50 分束器 (directional coupler)\n",
    "$$\\frac{\\sqrt{2}}{2}\n",
    "\\begin{pmatrix}\n",
    "1&i\\\\\n",
    "i&1\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "7. mzi 包括两种， 一种是先 $\\phi$后 $\\theta$，一种是先 $\\theta$后 $\\phi$, 对应的矩阵如下\n",
    "$$\n",
    "MZI_{PT} = \n",
    "ie^{i\\theta/2}\n",
    "\\begin{pmatrix}\n",
    "e^{i\\phi}\\sin{\\theta}&\\cos{\\theta}\\\\\n",
    "e^{i\\phi}\\cos{\\theta}&-\\sin{\\theta}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "$$\n",
    "MZI_{TP} = \n",
    "ie^{i\\theta/2}\n",
    "\\begin{pmatrix}\n",
    "e^{i\\phi}\\sin{\\theta}&e^{i\\phi}\\cos{\\theta}\\\\\n",
    "\\cos{\\theta}&-\\sin{\\theta}\\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "\n",
    "c. 线路需要可视化可以调用.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa0f2405",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:08.365202Z",
     "start_time": "2024-06-20T02:26:08.339227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"1.8181818181818181cm\" version=\"1.1\" width=\"20.1cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"82.5\" y=\"25\" /><text font-size=\"9\" x=\"80\" y=\"20\">PS</text><text font-size=\"7\" x=\"95\" y=\"20\">θ =0.0</text><polyline fill=\"none\" points=\"130,30 160,30 190,60 220,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,60 160,60 190,30 220,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"170\" y=\"25\">BS</text><text font-size=\"7\" x=\"185\" y=\"44\">θ =0.785</text><text font-size=\"7\" x=\"185\" y=\"50\">ϕ =1.571</text><polyline fill=\"none\" points=\"220,30 250,30 280,60 310,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,60 250,60 280,30 310,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"251\" y=\"25\">BS-RX</text><text font-size=\"7\" x=\"275\" y=\"44\">θ =1.571</text><polyline fill=\"none\" points=\"310,30 340,30 370,60 400,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,60 340,60 370,30 400,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"344\" y=\"25\">BS-H</text><text font-size=\"7\" x=\"365\" y=\"44\">θ =0.855</text><polyline fill=\"none\" points=\"400,30 430,30 460,60 490,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,60 430,60 460,30 490,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"431\" y=\"25\">BS-RX</text><text font-size=\"7\" x=\"455\" y=\"44\">θ =5.546</text><polyline fill=\"none\" points=\"490,30 520,30 550,60 580,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,60 520,60 550,30 580,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"521\" y=\"25\">BS-RY</text><text font-size=\"7\" x=\"545\" y=\"44\">θ =0.543</text><polyline fill=\"none\" points=\"580,30 610,30 640,60 670,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,60 610,60 640,30 670,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"608\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"635\" y=\"44\">θ =1.295</text><text font-size=\"7\" x=\"635\" y=\"50\">ϕ =4.517</text><polyline fill=\"none\" points=\"40,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd3562fa30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmode = 2\n",
    "ini_state1 = [1,1]\n",
    "dq_gate = dq.QumodeCircuit(nmode=nmode, init_state=ini_state1, name=\"test\", cutoff=sum(ini_state1)+1, basis=True)\n",
    "dq_gate.ps([0],0)  # 相位角为0， 作用第0个mode上\n",
    "dq_gate.bs_theta([0,1], np.pi/4) # 相位角为np.pi/4 作用在第0，1个mode上\n",
    "dq_gate.dc([0,1]) # 50:50 BS, special Rx\n",
    "dq_gate.bs_h([0,1]) # H gate\n",
    "dq_gate.bs_rx([0,1]) # Rx\n",
    "dq_gate.bs_ry([0,1])  #Ry\n",
    "dq_gate.mzi([0,1])\n",
    "dq_gate.draw() # 线路可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8d466d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:08.988798Z",
     "start_time": "2024-06-20T02:26:08.978983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0762-0.6112j,  0.7107-0.3398j],\n",
       "        [ 0.7875-0.0201j, -0.2097-0.5792j]], grad_fn=<MmBackward0>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dq_gate.get_unitary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fcbf6a",
   "metadata": {},
   "source": [
    "d. 线路演化调用dq_gate(), 演化结果用字典表示\\\n",
    "e. 自定义初态演dq_gate([1,0]) \\\n",
    "f. 得到线路对应的u 矩阵 dq_gate.get_unitary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5dc64274",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:09.932468Z",
     "start_time": "2024-06-20T02:26:09.843488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{|20>: tensor([-0.3704-0.5777j], grad_fn=<SelectBackward0>),\n",
       " |02>: tensor([-0.2500-0.6391j], grad_fn=<SelectBackward0>),\n",
       " |11>: tensor([0.2148-0.1095j], grad_fn=<SelectBackward0>)}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re = dq_gate() # 线路演化末态\n",
    "re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9aee78e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:10.451645Z",
     "start_time": "2024-06-20T02:26:10.435925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{|10>: tensor([0.7107-0.3398j], grad_fn=<SelectBackward0>),\n",
       " |01>: tensor([-0.2097-0.5792j], grad_fn=<SelectBackward0>)}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re2 = dq_gate(state=[0,1]) # 换不同初态进行相同线路演化\n",
    "re2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2ad55b62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:10.764294Z",
     "start_time": "2024-06-20T02:26:10.756434Z"
    }
   },
   "outputs": [],
   "source": [
    "# 获取线路对应的U矩阵\n",
    "u_mat = dq_gate.get_unitary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0061a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-29T11:46:46.193316Z",
     "start_time": "2023-11-29T11:46:46.170183Z"
    }
   },
   "source": [
    "## 支持任意的U矩阵输入"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9526ed9a",
   "metadata": {},
   "source": [
    "a. 调用dq_gate.any()可以加入任意U, 以下面6mode CNOT 为例(1/9 成功率) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "239fc147",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:12.752669Z",
     "start_time": "2024-06-20T02:26:12.739307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"5.454545454545454cm\" version=\"1.1\" width=\"3.9cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 60,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,60 60,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,90 60,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,90 130,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,120 60,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,120 130,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,150 60,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,150 130,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,180 60,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,180 130,180\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"cadetblue\" height=\"170\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"2\" width=\"50\" x=\"60\" y=\"20\" /><text font-size=\"10\" x=\"81\" y=\"105.0\">U</text><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text><text font-size=\"12\" x=\"25\" y=\"90\">2</text><text font-size=\"12\" x=\"25\" y=\"120\">3</text><text font-size=\"12\" x=\"25\" y=\"150\">4</text><text font-size=\"12\" x=\"25\" y=\"180\">5</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd3ab8c160>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unitary = np.array([[1, 0, 1, -1, 0, 0],\n",
    "                    [0, 1, 0 ,0, 0, np.sqrt(2)],\n",
    "                    [1, 0, 0, 1, 1, 0],\n",
    "                    [-1, 0, 1, 0, 1, 0],\n",
    "                    [0, 0, 1, 1, -1, 0],\n",
    "                    [0, np.sqrt(2), 0, 0, 0, -1]])/np.sqrt(3)\n",
    "nmode = 6\n",
    "ini_state1 = [1,0,1,0,0,0] ##最后两个mode辅助设为00\n",
    "dq_gate = dq.QumodeCircuit(nmode=nmode, init_state=ini_state1, name=\"test\", cutoff=sum(ini_state1)+1, basis=True)\n",
    "dq_gate.any(unitary, list(range(nmode)))\n",
    "dq_gate.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32963dce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:26:13.940027Z",
     "start_time": "2024-06-20T02:26:13.907981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{|000200>: tensor([-0.4714+0.j]),\n",
       " |200000>: tensor([0.4714+0.j]),\n",
       " |000110>: tensor([-0.3333+0.j]),\n",
       " |001010>: tensor([0.3333+0.j]),\n",
       " |001100>: tensor([0.3333+0.j]),\n",
       " |100010>: tensor([0.3333+0.j]),\n",
       " |101000>: tensor([0.3333+0.j]),\n",
       " |000002>: tensor([0.+0.j]),\n",
       " |000011>: tensor([0.+0.j]),\n",
       " |000020>: tensor([0.+0.j]),\n",
       " |000101>: tensor([0.+0.j]),\n",
       " |001001>: tensor([0.+0.j]),\n",
       " |002000>: tensor([0.+0.j]),\n",
       " |010001>: tensor([0.+0.j]),\n",
       " |010010>: tensor([0.+0.j]),\n",
       " |010100>: tensor([0.+0.j]),\n",
       " |011000>: tensor([0.+0.j]),\n",
       " |020000>: tensor([0.+0.j]),\n",
       " |100001>: tensor([0.+0.j]),\n",
       " |100100>: tensor([0.+0.j]),\n",
       " |110000>: tensor([0.+0.j])}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check CNOT 结果\n",
    "re = dq_gate()\n",
    "re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda67f70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-30T02:17:21.162004Z",
     "start_time": "2023-11-30T02:17:21.154255Z"
    }
   },
   "source": [
    "## 对演化结果测量采样"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03217201",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-15T02:30:12.717251Z",
     "start_time": "2023-12-15T02:30:12.713993Z"
    }
   },
   "source": [
    "dq_gate.measure() 可以对结果采样， 可以自定义采样的线路以及次数 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea29ccec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:52:39.887398Z",
     "start_time": "2024-06-19T08:52:39.830856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{|101000>: 111,\n",
       " |001100>: 118,\n",
       " |001010>: 101,\n",
       " |100010>: 117,\n",
       " |200000>: 222,\n",
       " |000200>: 208,\n",
       " |000110>: 123}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = dq_gate.measure(wires=[0,1,2,3,4,5], shots=1000)\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2d6b38",
   "metadata": {},
   "source": [
    "## 支持batch输入"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcece21",
   "metadata": {},
   "source": [
    "### 支持初态的batch输入"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14689a5e",
   "metadata": {},
   "source": [
    " 支持叠加态的输入， 对于叠加态 [($\\frac{\\sqrt{2}}{2}$, [1,0,1,0]),($\\frac{\\sqrt2}{2}$, [0,1,1,0])] 此时basis=False， 使用张量计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "026e4bae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:55:13.737908Z",
     "start_time": "2024-06-19T08:55:13.719547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"5.454545454545454cm\" version=\"1.1\" width=\"3.9cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 60,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,60 60,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,90 60,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,90 130,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,120 60,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,120 130,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,150 60,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,150 130,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,180 60,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"110,180 130,180\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"cadetblue\" height=\"170\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"2\" width=\"50\" x=\"60\" y=\"20\" /><text font-size=\"10\" x=\"81\" y=\"105.0\">U</text><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text><text font-size=\"12\" x=\"25\" y=\"90\">2</text><text font-size=\"12\" x=\"25\" y=\"120\">3</text><text font-size=\"12\" x=\"25\" y=\"150\">4</text><text font-size=\"12\" x=\"25\" y=\"180\">5</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd33f163b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = torch.zeros([2, 3, 3, 3, 3, 3, 3])*1j\n",
    "ini_state1 = [(np.sqrt(3)/3, [0, 1, 1, 0, 0, 0]), (np.sqrt(3)/3*1j, [0, 1, 0, 1, 0, 0]), (np.sqrt(3)/3,[0,0,0,0,1,1])]\n",
    "ini_state2 = [(np.sqrt(2)/2, [0, 1, 1, 0, 0, 0]), (np.sqrt(2)/2*1j, [0, 1, 0, 1, 0, 0])]\n",
    "test[0] = dq.photonic.state.FockState(state=ini_state1, basis=False).state  # 转为张量\n",
    "test[1] = dq.photonic.state.FockState(state=ini_state2, basis=False).state\n",
    "nmode = 6\n",
    "dq_gate = dq.QumodeCircuit(nmode=nmode, init_state=test, name=\"test\", basis=False)\n",
    "dq_gate.any(unitary, list(range(nmode)))\n",
    "dq_gate.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "05b1169b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:55:14.607499Z",
     "start_time": "2024-06-19T08:55:14.599666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_0: (0.577+0.000j)|000011> + (0.000+0.577j)|010100> + (0.577+0.000j)|011000>\n",
       "state_1: (0.000+0.707j)|010100> + (0.707+0.000j)|011000>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出初态\n",
    "dq_gate.init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f208f160",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:55:56.325616Z",
     "start_time": "2024-06-19T08:55:15.094316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3, 3, 3, 3, 3, 3])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re = dq_gate()\n",
    "re.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337e009e",
   "metadata": {},
   "source": [
    "### 支持线路参数的batch 输入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7db3ff20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:54:18.572953Z",
     "start_time": "2024-06-19T08:54:18.550965Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"3.6363636363636362cm\" version=\"1.1\" width=\"6.6cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 70,30 100,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,60 70,60 100,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"80\" y=\"25\">BS</text><text font-size=\"7\" x=\"95\" y=\"44\">θ =0.785</text><text font-size=\"7\" x=\"95\" y=\"50\">ϕ =1.571</text><polyline fill=\"none\" points=\"40,90 70,90 100,120 130,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,120 70,120 100,90 130,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"80\" y=\"85\">BS</text><text font-size=\"7\" x=\"95\" y=\"104\">θ =0.785</text><text font-size=\"7\" x=\"95\" y=\"110\">ϕ =1.571</text><polyline fill=\"none\" points=\"130,60 160,60 190,90 220,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,90 160,90 190,60 220,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"170\" y=\"55\">BS</text><text font-size=\"7\" x=\"185\" y=\"74\">θ =0.785</text><text font-size=\"7\" x=\"185\" y=\"80\">ϕ =1.571</text><polyline fill=\"none\" points=\"130,30 220,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,120 220,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text><text font-size=\"12\" x=\"25\" y=\"90\">2</text><text font-size=\"12\" x=\"25\" y=\"120\">3</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd33f5b040>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ini_state1 = [1,1,1,1]\n",
    "ini_state2 = [(1, [1,1,1,1])]\n",
    "\n",
    "test_circ1 = dq.QumodeCircuit(nmode=4, init_state=ini_state1, name=\"test1\", cutoff=5, basis=True) # using tensor state\n",
    "test_circ2 = dq.QumodeCircuit(nmode=4, init_state=ini_state2, name=\"test2\", cutoff=5, basis=False) # using tensor state\n",
    "\n",
    "\n",
    "test_circ1.bs_theta([0, 1],np.pi/4, encode=True)\n",
    "test_circ1.bs_theta([2, 3],np.pi/4, encode=True)\n",
    "test_circ1.bs_theta([1, 2],np.pi/4, encode=True)\n",
    "\n",
    "\n",
    "test_circ2.bs_theta([0, 1],np.pi/4, encode=True)\n",
    "test_circ2.bs_theta([2, 3],np.pi/4, encode=True)\n",
    "test_circ2.bs_theta([1, 2],np.pi/4, encode=True)\n",
    "\n",
    "test_circ1.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3af785f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:56:07.373487Z",
     "start_time": "2024-06-19T08:56:07.365834Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.7854, 0.7854, 0.7854],\n",
      "        [0.7854, 0.7854, 0.7854]])\n"
     ]
    }
   ],
   "source": [
    "# encoding theta data\n",
    "nparas = 3\n",
    "batch = 2\n",
    "data = (torch.tensor([np.pi/4]*batch*nparas).reshape(batch, nparas))\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7f9acb81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T08:56:07.993685Z",
     "start_time": "2024-06-19T08:56:07.943883Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{|2002>: tensor([[-0.5000-1.4901e-08j],\n",
       "         [-0.5000-1.4901e-08j]]),\n",
       " |0112>: tensor([[3.4420e-08-0.3536j],\n",
       "         [3.4420e-08-0.3536j]]),\n",
       " |2110>: tensor([[-3.4420e-08-0.3536j],\n",
       "         [-3.4420e-08-0.3536j]]),\n",
       " |0400>: tensor([[0.3062-2.8997e-08j],\n",
       "         [0.3062-2.8997e-08j]]),\n",
       " |0040>: tensor([[0.3062+6.2861e-08j],\n",
       "         [0.3062+6.2861e-08j]]),\n",
       " |0202>: tensor([[-0.2500+0.j],\n",
       "         [-0.2500+0.j]]),\n",
       " |0220>: tensor([[0.2500+4.9605e-10j],\n",
       "         [0.2500+4.9605e-10j]]),\n",
       " |2020>: tensor([[-0.2500+5.9605e-08j],\n",
       "         [-0.2500+5.9605e-08j]]),\n",
       " |0022>: tensor([[0.2500+5.9605e-08j],\n",
       "         [0.2500+5.9605e-08j]]),\n",
       " |2200>: tensor([[0.2500-8.9407e-08j],\n",
       "         [0.2500-8.9407e-08j]]),\n",
       " |1120>: tensor([[1.2644e-07+0.j],\n",
       "         [1.2644e-07+0.j]]),\n",
       " |0301>: tensor([[7.3000e-08-4.8667e-08j],\n",
       "         [7.3000e-08-4.8667e-08j]]),\n",
       " |1111>: tensor([[-1.3232e-08-5.9605e-08j],\n",
       "         [-1.3232e-08-5.9605e-08j]]),\n",
       " |1102>: tensor([[4.2147e-08+4.2147e-08j],\n",
       "         [4.2147e-08+4.2147e-08j]]),\n",
       " |0031>: tensor([[2.4333e-08-4.8667e-08j],\n",
       "         [2.4333e-08-4.8667e-08j]]),\n",
       " |1030>: tensor([[4.8667e-08+2.4333e-08j],\n",
       "         [4.8667e-08+2.4333e-08j]]),\n",
       " |0103>: tensor([[4.8667e-08+0.j],\n",
       "         [4.8667e-08+0.j]]),\n",
       " |3100>: tensor([[0.-4.8667e-08j],\n",
       "         [0.-4.8667e-08j]]),\n",
       " |1021>: tensor([[1.9519e-08-4.2147e-08j],\n",
       "         [1.9519e-08-4.2147e-08j]]),\n",
       " |1201>: tensor([[1.4453e-08-4.2147e-08j],\n",
       "         [1.4453e-08-4.2147e-08j]]),\n",
       " |0121>: tensor([[0.+4.2147e-08j],\n",
       "         [0.+4.2147e-08j]]),\n",
       " |1012>: tensor([[0.-4.2147e-08j],\n",
       "         [0.-4.2147e-08j]]),\n",
       " |1210>: tensor([[0.-4.2147e-08j],\n",
       "         [0.-4.2147e-08j]]),\n",
       " |2101>: tensor([[-4.2147e-08+0.j],\n",
       "         [-4.2147e-08+0.j]]),\n",
       " |1300>: tensor([[-2.4333e-08-2.4333e-08j],\n",
       "         [-2.4333e-08-2.4333e-08j]]),\n",
       " |0013>: tensor([[-2.4333e-08+0.j],\n",
       "         [-2.4333e-08+0.j]]),\n",
       " |3010>: tensor([[0.-2.4333e-08j],\n",
       "         [0.-2.4333e-08j]]),\n",
       " |0130>: tensor([[0.+2.1899e-08j],\n",
       "         [0.+2.1899e-08j]]),\n",
       " |3001>: tensor([[0.-1.5006e-08j],\n",
       "         [0.-1.5006e-08j]]),\n",
       " |1003>: tensor([[0.-3.2442e-09j],\n",
       "         [0.-3.2442e-09j]]),\n",
       " |0310>: tensor([[0.+3.2442e-09j],\n",
       "         [0.+3.2442e-09j]]),\n",
       " |0004>: tensor([[0.-2.9008e-15j],\n",
       "         [0.-2.9008e-15j]]),\n",
       " |4000>: tensor([[0.+2.9008e-15j],\n",
       "         [0.+2.9008e-15j]]),\n",
       " |0211>: tensor([[0.+0.j],\n",
       "         [0.+0.j]]),\n",
       " |2011>: tensor([[0.+0.j],\n",
       "         [0.+0.j]])}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re1_data = test_circ1(data=data)\n",
    "re1_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c12526",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-29T11:47:30.896043Z",
     "start_time": "2023-11-29T11:47:30.881202Z"
    }
   },
   "source": [
    "## 支持简单的噪声模拟（Gaussian noise）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac5c2c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-15T03:16:39.952528Z",
     "start_time": "2023-12-15T03:16:39.948885Z"
    }
   },
   "source": [
    "噪声模拟使用dq.QumodeCircuit(noise=True), 这里默认所有参数的噪声都是高斯分布，平均值为0, 标准差为0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "24accee6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:56:48.848633Z",
     "start_time": "2024-06-20T02:56:48.420506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 构建CNOT线路例子\n",
    "u6x6 = np.array([[1, 0, 1, -1, 0, 0],\n",
    "                 [0, 1, 0 ,0,  0, np.sqrt(2)],\n",
    "                 [1,  0, 0, 1, 1, 0],\n",
    "                 [-1, 0, 1, 0, 1, 0],\n",
    "                 [0,  0, 1, 1, -1,0],\n",
    "                 [0, np.sqrt(2), 0,0,0,-1]])/np.sqrt(3)\n",
    "\n",
    "ud = dq.UnitaryDecomposer(u6x6)\n",
    "mzi_info = ud.decomp()\n",
    "p_mzi = dq.photonic.draw.DrawClements(6, mzi_info[0],cl='black')\n",
    "p_mzi.plotting_clements()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71989f5f",
   "metadata": {},
   "source": [
    "可以通过deepquantum 手动搭建上图的clements 线路"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "41a8b920",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:31:18.615050Z",
     "start_time": "2024-06-20T02:31:18.549799Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"5.454545454545454cm\" version=\"1.1\" width=\"66.0cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"82.5\" y=\"25\" /><text font-size=\"9\" x=\"80\" y=\"20\">PS</text><text font-size=\"7\" x=\"95\" y=\"20\">θ =2.996</text><polyline fill=\"none\" points=\"130,30 160,30 190,60 220,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,60 160,60 190,30 220,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"170\" y=\"25\">BS</text><text font-size=\"7\" x=\"185\" y=\"44\">θ =0.786</text><text font-size=\"7\" x=\"185\" y=\"50\">ϕ =1.622</text><polyline fill=\"none\" points=\"220,30 310,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"262.5\" y=\"25\" /><text font-size=\"9\" x=\"260\" y=\"20\">PS</text><text font-size=\"7\" x=\"275\" y=\"20\">θ =2.922</text><polyline fill=\"none\" points=\"310,30 340,30 370,60 400,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,60 340,60 370,30 400,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"350\" y=\"25\">BS</text><text font-size=\"7\" x=\"365\" y=\"44\">θ =0.856</text><text font-size=\"7\" x=\"365\" y=\"50\">ϕ =1.608</text><polyline fill=\"none\" points=\"40,90 130,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"82.5\" y=\"85\" /><text font-size=\"9\" x=\"80\" y=\"80\">PS</text><text font-size=\"7\" x=\"95\" y=\"80\">θ =3.08</text><polyline fill=\"none\" points=\"130,90 160,90 190,120 220,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,120 160,120 190,90 220,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"170\" y=\"85\">BS</text><text font-size=\"7\" x=\"185\" y=\"104\">θ =0.863</text><text font-size=\"7\" x=\"185\" y=\"110\">ϕ =1.658</text><polyline fill=\"none\" points=\"220,90 310,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"262.5\" y=\"85\" /><text font-size=\"9\" x=\"260\" y=\"80\">PS</text><text font-size=\"7\" x=\"275\" y=\"80\">θ =1.519</text><polyline fill=\"none\" points=\"310,90 340,90 370,120 400,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,120 340,120 370,90 400,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"350\" y=\"85\">BS</text><text font-size=\"7\" x=\"365\" y=\"104\">θ =0.605</text><text font-size=\"7\" x=\"365\" y=\"110\">ϕ =1.686</text><polyline fill=\"none\" points=\"40,150 130,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"82.5\" y=\"145\" /><text font-size=\"9\" x=\"80\" y=\"140\">PS</text><text font-size=\"7\" x=\"95\" y=\"140\">θ =0.216</text><polyline fill=\"none\" points=\"130,150 160,150 190,180 220,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,180 160,180 190,150 220,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"170\" y=\"145\">BS</text><text font-size=\"7\" x=\"185\" y=\"164\">θ =0.845</text><text font-size=\"7\" x=\"185\" y=\"170\">ϕ =1.541</text><polyline fill=\"none\" points=\"220,150 310,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"262.5\" y=\"145\" /><text font-size=\"9\" x=\"260\" y=\"140\">PS</text><text font-size=\"7\" x=\"275\" y=\"140\">θ =2.498</text><polyline fill=\"none\" points=\"310,150 340,150 370,180 400,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,180 340,180 370,150 400,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"350\" y=\"145\">BS</text><text font-size=\"7\" x=\"365\" y=\"164\">θ =1.015</text><text font-size=\"7\" x=\"365\" y=\"170\">ϕ =1.366</text><polyline fill=\"none\" points=\"400,60 490,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"442.5\" y=\"55\" /><text font-size=\"9\" x=\"440\" y=\"50\">PS</text><text font-size=\"7\" x=\"455\" y=\"50\">θ =5.987</text><polyline fill=\"none\" points=\"490,60 520,60 550,90 580,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,90 520,90 550,60 580,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"530\" y=\"55\">BS</text><text font-size=\"7\" x=\"545\" y=\"74\">θ =0.86</text><text font-size=\"7\" x=\"545\" y=\"80\">ϕ =1.661</text><polyline fill=\"none\" points=\"580,60 670,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"55\" /><text font-size=\"9\" x=\"620\" y=\"50\">PS</text><text font-size=\"7\" x=\"635\" y=\"50\">θ =0.092</text><polyline fill=\"none\" points=\"670,60 700,60 730,90 760,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"670,90 700,90 730,60 760,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"710\" y=\"55\">BS</text><text font-size=\"7\" x=\"725\" y=\"74\">θ =0.653</text><text font-size=\"7\" x=\"725\" y=\"80\">ϕ =1.38</text><polyline fill=\"none\" points=\"400,120 490,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"442.5\" y=\"115\" /><text font-size=\"9\" x=\"440\" y=\"110\">PS</text><text font-size=\"7\" x=\"455\" y=\"110\">θ =3.892</text><polyline fill=\"none\" points=\"490,120 520,120 550,150 580,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,150 520,150 550,120 580,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"530\" y=\"115\">BS</text><text font-size=\"7\" x=\"545\" y=\"134\">θ =0.528</text><text font-size=\"7\" x=\"545\" y=\"140\">ϕ =1.568</text><polyline fill=\"none\" points=\"580,120 670,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"115\" /><text font-size=\"9\" x=\"620\" y=\"110\">PS</text><text font-size=\"7\" x=\"635\" y=\"110\">θ =1.339</text><polyline fill=\"none\" points=\"670,120 700,120 730,150 760,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"670,150 700,150 730,120 760,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"710\" y=\"115\">BS</text><text font-size=\"7\" x=\"725\" y=\"134\">θ =0.625</text><text font-size=\"7\" x=\"725\" y=\"140\">ϕ =1.637</text><polyline fill=\"none\" points=\"400,30 490,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"442.5\" y=\"25\" /><text font-size=\"9\" x=\"440\" y=\"20\">PS</text><text font-size=\"7\" x=\"455\" y=\"20\">θ =0.83</text><polyline fill=\"none\" points=\"760,30 790,30 820,60 850,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"760,60 790,60 820,30 850,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"800\" y=\"25\">BS</text><text font-size=\"7\" x=\"815\" y=\"44\">θ =0.673</text><text font-size=\"7\" x=\"815\" y=\"50\">ϕ =1.77</text><polyline fill=\"none\" points=\"850,30 940,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"892.5\" y=\"25\" /><text font-size=\"9\" x=\"890\" y=\"20\">PS</text><text font-size=\"7\" x=\"905\" y=\"20\">θ =1.25</text><polyline fill=\"none\" points=\"940,30 970,30 1000,60 1030,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"940,60 970,60 1000,30 1030,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"980\" y=\"25\">BS</text><text font-size=\"7\" x=\"995\" y=\"44\">θ =0.66</text><text font-size=\"7\" x=\"995\" y=\"50\">ϕ =1.587</text><polyline fill=\"none\" points=\"760,90 850,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"802.5\" y=\"85\" /><text font-size=\"9\" x=\"800\" y=\"80\">PS</text><text font-size=\"7\" x=\"815\" y=\"80\">θ =3.691</text><polyline fill=\"none\" points=\"850,90 880,90 910,120 940,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"850,120 880,120 910,90 940,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"890\" y=\"85\">BS</text><text font-size=\"7\" x=\"905\" y=\"104\">θ =0.709</text><text font-size=\"7\" x=\"905\" y=\"110\">ϕ =1.586</text><polyline fill=\"none\" points=\"940,90 1030,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"982.5\" y=\"85\" /><text font-size=\"9\" x=\"980\" y=\"80\">PS</text><text font-size=\"7\" x=\"995\" y=\"80\">θ =0.408</text><polyline fill=\"none\" points=\"1030,90 1060,90 1090,120 1120,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1030,120 1060,120 1090,90 1120,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1070\" y=\"85\">BS</text><text font-size=\"7\" x=\"1085\" y=\"104\">θ =0.676</text><text font-size=\"7\" x=\"1085\" y=\"110\">ϕ =1.416</text><polyline fill=\"none\" points=\"760,150 850,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"802.5\" y=\"145\" /><text font-size=\"9\" x=\"800\" y=\"140\">PS</text><text font-size=\"7\" x=\"815\" y=\"140\">θ =4.172</text><polyline fill=\"none\" points=\"850,150 880,150 910,180 940,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"850,180 880,180 910,150 940,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"890\" y=\"145\">BS</text><text font-size=\"7\" x=\"905\" y=\"164\">θ =0.732</text><text font-size=\"7\" x=\"905\" y=\"170\">ϕ =1.565</text><polyline fill=\"none\" points=\"940,150 1030,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"982.5\" y=\"145\" /><text font-size=\"9\" x=\"980\" y=\"140\">PS</text><text font-size=\"7\" x=\"995\" y=\"140\">θ =0.441</text><polyline fill=\"none\" points=\"1030,150 1060,150 1090,180 1120,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1030,180 1060,180 1090,150 1120,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1070\" y=\"145\">BS</text><text font-size=\"7\" x=\"1085\" y=\"164\">θ =0.749</text><text font-size=\"7\" x=\"1085\" y=\"170\">ϕ =1.517</text><polyline fill=\"none\" points=\"1030,60 1120,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1072.5\" y=\"55\" /><text font-size=\"9\" x=\"1070\" y=\"50\">PS</text><text font-size=\"7\" x=\"1085\" y=\"50\">θ =4.081</text><polyline fill=\"none\" points=\"1120,60 1150,60 1180,90 1210,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1120,90 1150,90 1180,60 1210,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1160\" y=\"55\">BS</text><text font-size=\"7\" x=\"1175\" y=\"74\">θ =0.806</text><text font-size=\"7\" x=\"1175\" y=\"80\">ϕ =1.582</text><polyline fill=\"none\" points=\"1210,60 1300,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1252.5\" y=\"55\" /><text font-size=\"9\" x=\"1250\" y=\"50\">PS</text><text font-size=\"7\" x=\"1265\" y=\"50\">θ =1.379</text><polyline fill=\"none\" points=\"1300,60 1330,60 1360,90 1390,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1300,90 1330,90 1360,60 1390,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1340\" y=\"55\">BS</text><text font-size=\"7\" x=\"1355\" y=\"74\">θ =0.732</text><text font-size=\"7\" x=\"1355\" y=\"80\">ϕ =1.609</text><polyline fill=\"none\" points=\"1120,120 1210,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1162.5\" y=\"115\" /><text font-size=\"9\" x=\"1160\" y=\"110\">PS</text><text font-size=\"7\" x=\"1175\" y=\"110\">θ =0.989</text><polyline fill=\"none\" points=\"1210,120 1240,120 1270,150 1300,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1210,150 1240,150 1270,120 1300,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1250\" y=\"115\">BS</text><text font-size=\"7\" x=\"1265\" y=\"134\">θ =1.001</text><text font-size=\"7\" x=\"1265\" y=\"140\">ϕ =1.546</text><polyline fill=\"none\" points=\"1300,120 1390,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1342.5\" y=\"115\" /><text font-size=\"9\" x=\"1340\" y=\"110\">PS</text><text font-size=\"7\" x=\"1355\" y=\"110\">θ =1.179</text><polyline fill=\"none\" points=\"1390,120 1420,120 1450,150 1480,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1390,150 1420,150 1450,120 1480,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1430\" y=\"115\">BS</text><text font-size=\"7\" x=\"1445\" y=\"134\">θ =0.921</text><text font-size=\"7\" x=\"1445\" y=\"140\">ϕ =1.651</text><polyline fill=\"none\" points=\"1030,30 1120,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1072.5\" y=\"25\" /><text font-size=\"9\" x=\"1070\" y=\"20\">PS</text><text font-size=\"7\" x=\"1085\" y=\"20\">θ =6.182</text><polyline fill=\"none\" points=\"1390,30 1420,30 1450,60 1480,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1390,60 1420,60 1450,30 1480,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1430\" y=\"25\">BS</text><text font-size=\"7\" x=\"1445\" y=\"44\">θ =0.818</text><text font-size=\"7\" x=\"1445\" y=\"50\">ϕ =1.493</text><polyline fill=\"none\" points=\"1480,30 1570,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1522.5\" y=\"25\" /><text font-size=\"9\" x=\"1520\" y=\"20\">PS</text><text font-size=\"7\" x=\"1535\" y=\"20\">θ =3.121</text><polyline fill=\"none\" points=\"1570,30 1600,30 1630,60 1660,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1570,60 1600,60 1630,30 1660,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1610\" y=\"25\">BS</text><text font-size=\"7\" x=\"1625\" y=\"44\">θ =0.81</text><text font-size=\"7\" x=\"1625\" y=\"50\">ϕ =1.617</text><polyline fill=\"none\" points=\"1390,90 1480,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1432.5\" y=\"85\" /><text font-size=\"9\" x=\"1430\" y=\"80\">PS</text><text font-size=\"7\" x=\"1445\" y=\"80\">θ =3.869</text><polyline fill=\"none\" points=\"1480,90 1510,90 1540,120 1570,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1480,120 1510,120 1540,90 1570,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1520\" y=\"85\">BS</text><text font-size=\"7\" x=\"1535\" y=\"104\">θ =0.833</text><text font-size=\"7\" x=\"1535\" y=\"110\">ϕ =1.468</text><polyline fill=\"none\" points=\"1570,90 1660,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1612.5\" y=\"85\" /><text font-size=\"9\" x=\"1610\" y=\"80\">PS</text><text font-size=\"7\" x=\"1625\" y=\"80\">θ =0.226</text><polyline fill=\"none\" points=\"1660,90 1690,90 1720,120 1750,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1660,120 1690,120 1720,90 1750,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1700\" y=\"85\">BS</text><text font-size=\"7\" x=\"1715\" y=\"104\">θ =0.682</text><text font-size=\"7\" x=\"1715\" y=\"110\">ϕ =1.451</text><polyline fill=\"none\" points=\"1480,150 1570,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1522.5\" y=\"145\" /><text font-size=\"9\" x=\"1520\" y=\"140\">PS</text><text font-size=\"7\" x=\"1535\" y=\"140\">θ =1.087</text><polyline fill=\"none\" points=\"1570,150 1600,150 1630,180 1660,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1570,180 1600,180 1630,150 1660,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1610\" y=\"145\">BS</text><text font-size=\"7\" x=\"1625\" y=\"164\">θ =0.695</text><text font-size=\"7\" x=\"1625\" y=\"170\">ϕ =1.758</text><polyline fill=\"none\" points=\"1660,150 1750,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1702.5\" y=\"145\" /><text font-size=\"9\" x=\"1700\" y=\"140\">PS</text><text font-size=\"7\" x=\"1715\" y=\"140\">θ =-0.1</text><polyline fill=\"none\" points=\"1750,150 1780,150 1810,180 1840,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1750,180 1780,180 1810,150 1840,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1790\" y=\"145\">BS</text><text font-size=\"7\" x=\"1805\" y=\"164\">θ =0.73</text><text font-size=\"7\" x=\"1805\" y=\"170\">ϕ =1.667</text><polyline fill=\"none\" points=\"1660,60 1750,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1702.5\" y=\"55\" /><text font-size=\"9\" x=\"1700\" y=\"50\">PS</text><text font-size=\"7\" x=\"1715\" y=\"50\">θ =2.608</text><polyline fill=\"none\" points=\"1750,60 1780,60 1810,90 1840,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1750,90 1780,90 1810,60 1840,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1790\" y=\"55\">BS</text><text font-size=\"7\" x=\"1805\" y=\"74\">θ =0.634</text><text font-size=\"7\" x=\"1805\" y=\"80\">ϕ =1.834</text><polyline fill=\"none\" points=\"1840,60 1930,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1882.5\" y=\"55\" /><text font-size=\"9\" x=\"1880\" y=\"50\">PS</text><text font-size=\"7\" x=\"1895\" y=\"50\">θ =0.43</text><polyline fill=\"none\" points=\"1930,60 1960,60 1990,90 2020,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1930,90 1960,90 1990,60 2020,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1970\" y=\"55\">BS</text><text font-size=\"7\" x=\"1985\" y=\"74\">θ =0.741</text><text font-size=\"7\" x=\"1985\" y=\"80\">ϕ =1.463</text><polyline fill=\"none\" points=\"1750,120 1840,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1792.5\" y=\"115\" /><text font-size=\"9\" x=\"1790\" y=\"110\">PS</text><text font-size=\"7\" x=\"1805\" y=\"110\">θ =0.767</text><polyline fill=\"none\" points=\"1840,120 1870,120 1900,150 1930,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1840,150 1870,150 1900,120 1930,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"1880\" y=\"115\">BS</text><text font-size=\"7\" x=\"1895\" y=\"134\">θ =0.555</text><text font-size=\"7\" x=\"1895\" y=\"140\">ϕ =1.523</text><polyline fill=\"none\" points=\"1930,120 2020,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1972.5\" y=\"115\" /><text font-size=\"9\" x=\"1970\" y=\"110\">PS</text><text font-size=\"7\" x=\"1985\" y=\"110\">θ =3.128</text><polyline fill=\"none\" points=\"2020,120 2050,120 2080,150 2110,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2020,150 2050,150 2080,120 2110,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"2060\" y=\"115\">BS</text><text font-size=\"7\" x=\"2075\" y=\"134\">θ =0.838</text><text font-size=\"7\" x=\"2075\" y=\"140\">ϕ =1.634</text><polyline fill=\"none\" points=\"1660,30 1750,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1702.5\" y=\"25\" /><text font-size=\"9\" x=\"1700\" y=\"20\">PS</text><text font-size=\"7\" x=\"1715\" y=\"20\">θ =0.438</text><polyline fill=\"none\" points=\"2020,60 2110,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"2062.5\" y=\"55\" /><text font-size=\"9\" x=\"2060\" y=\"50\">PS</text><text font-size=\"7\" x=\"2075\" y=\"50\">θ =5.449</text><polyline fill=\"none\" points=\"2020,90 2110,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"2062.5\" y=\"85\" /><text font-size=\"9\" x=\"2060\" y=\"80\">PS</text><text font-size=\"7\" x=\"2075\" y=\"80\">θ =5.036</text><polyline fill=\"none\" points=\"2110,120 2200,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"2152.5\" y=\"115\" /><text font-size=\"9\" x=\"2150\" y=\"110\">PS</text><text font-size=\"7\" x=\"2165\" y=\"110\">θ =3.052</text><polyline fill=\"none\" points=\"2110,150 2200,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"2152.5\" y=\"145\" /><text font-size=\"9\" x=\"2150\" y=\"140\">PS</text><text font-size=\"7\" x=\"2165\" y=\"140\">θ =6.046</text><polyline fill=\"none\" points=\"1840,180 1930,180\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"1882.5\" y=\"175\" /><text font-size=\"9\" x=\"1880\" y=\"170\">PS</text><text font-size=\"7\" x=\"1895\" y=\"170\">θ =6.206</text><polyline fill=\"none\" points=\"40,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,120 130,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,180 130,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,60 310,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,120 310,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,180 310,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,90 490,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,150 490,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,180 490,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,30 580,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,180 580,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,30 670,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,90 670,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,150 670,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,180 670,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"670,30 760,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"670,180 760,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"760,120 850,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"760,180 850,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"850,60 940,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"940,120 1030,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"940,180 1030,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1120,30 1210,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1120,150 1210,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1120,180 1210,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1210,30 1300,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1210,90 1300,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1210,180 1300,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1300,30 1390,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1300,150 1390,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1300,180 1390,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1390,180 1480,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1480,60 1570,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1480,180 1570,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1570,120 1660,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1660,180 1750,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1750,30 1840,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1840,30 1930,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1840,90 1930,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1930,30 2020,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1930,150 2020,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"1930,180 2020,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2020,30 2110,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2020,180 2110,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2110,30 2200,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2110,60 2200,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2110,90 2200,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"2110,180 2200,180\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text><text font-size=\"12\" x=\"25\" y=\"90\">2</text><text font-size=\"12\" x=\"25\" y=\"120\">3</text><text font-size=\"12\" x=\"25\" y=\"150\">4</text><text font-size=\"12\" x=\"25\" y=\"180\">5</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd31877220>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ini_state = [1,0,1,0,0,0]\n",
    "dic_mzi = mzi_info[1]\n",
    "phase_angle = mzi_info[0]['phase_angle']\n",
    "test_cnot_noise =dq.QumodeCircuit(nmode=6, init_state=ini_state, name=\"test2\", cutoff = 3, basis=True, noise=True) # using state list\n",
    "\n",
    "for i in range(3):\n",
    "    for j in [0,2,4]:\n",
    "        temp_key = (j, j+1)\n",
    "        para1, para2 = dic_mzi[temp_key][i]\n",
    "        test_cnot_noise.ps([j],para1)\n",
    "        test_cnot_noise.bs_theta([j, j+1], np.pi/4)\n",
    "        test_cnot_noise.ps([j], para2)\n",
    "        test_cnot_noise.bs_theta([j, j+1],np.pi/4)\n",
    "\n",
    "    for j in [1,3]:\n",
    "        temp_key = (j, j+1)\n",
    "        para1, para2 = dic_mzi[temp_key][i]\n",
    "        test_cnot_noise.ps([j], para1)\n",
    "        test_cnot_noise.bs_theta([j, j+1], np.pi/4)\n",
    "        test_cnot_noise.ps([j], para2)\n",
    "        test_cnot_noise.bs_theta([j, j+1],np.pi/4)\n",
    "for k in range(6):\n",
    "     test_cnot_noise.ps([k], phase_angle[k])\n",
    "test_cnot_noise.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fd511f03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:31:19.275588Z",
     "start_time": "2024-06-20T02:31:19.251669Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.6986)\n"
     ]
    }
   ],
   "source": [
    "unitary = test_cnot_noise.get_unitary()\n",
    "temp_2 = torch.trace(unitary.H @ torch.tensor(u6x6, dtype=torch.cfloat))\n",
    "fidelity_1 = abs(temp_2/6)**2\n",
    "print(fidelity_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df36d1a0",
   "metadata": {},
   "source": [
    "## Clements 架构光量子线路"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623daca0",
   "metadata": {},
   "source": [
    "使用Clements 模块构建相同的CNOT光量子线路"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c6b45a0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:27:42.088905Z",
     "start_time": "2024-06-20T02:27:42.003997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"5.454545454545454cm\" version=\"1.1\" width=\"20.1cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 70,30 100,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,60 70,60 100,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"68\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"95\" y=\"44\">θ =3.142</text><text font-size=\"7\" x=\"95\" y=\"50\">ϕ =3.142</text><polyline fill=\"none\" points=\"40,90 70,90 100,120 130,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,120 70,120 100,90 130,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"68\" y=\"85\">MZI-PT</text><text font-size=\"7\" x=\"95\" y=\"104\">θ =1.571</text><text font-size=\"7\" x=\"95\" y=\"110\">ϕ =3.142</text><polyline fill=\"none\" points=\"40,150 70,150 100,180 130,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"40,180 70,180 100,150 130,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"68\" y=\"145\">MZI-PT</text><text font-size=\"7\" x=\"95\" y=\"164\">θ =2.587</text><text font-size=\"7\" x=\"95\" y=\"170\">ϕ =0.319</text><polyline fill=\"none\" points=\"130,60 160,60 190,90 220,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,90 160,90 190,60 220,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"158\" y=\"55\">MZI-PT</text><text font-size=\"7\" x=\"185\" y=\"74\">θ =0.0</text><text font-size=\"7\" x=\"185\" y=\"80\">ϕ =6.079</text><polyline fill=\"none\" points=\"130,120 160,120 190,150 220,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,150 160,150 190,120 220,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"158\" y=\"115\">MZI-PT</text><text font-size=\"7\" x=\"185\" y=\"134\">θ =1.194</text><text font-size=\"7\" x=\"185\" y=\"140\">ϕ =3.968</text><polyline fill=\"none\" points=\"220,30 250,30 280,60 310,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,60 250,60 280,30 310,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"248\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"275\" y=\"44\">θ =1.231</text><text font-size=\"7\" x=\"275\" y=\"50\">ϕ =0.785</text><polyline fill=\"none\" points=\"220,90 250,90 280,120 310,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,120 250,120 280,90 310,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"248\" y=\"85\">MZI-PT</text><text font-size=\"7\" x=\"275\" y=\"104\">θ =0.317</text><text font-size=\"7\" x=\"275\" y=\"110\">ϕ =3.666</text><polyline fill=\"none\" points=\"220,150 250,150 280,180 310,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,180 250,180 280,150 310,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"248\" y=\"145\">MZI-PT</text><text font-size=\"7\" x=\"275\" y=\"164\">θ =0.317</text><text font-size=\"7\" x=\"275\" y=\"170\">ϕ =4.115</text><polyline fill=\"none\" points=\"310,60 340,60 370,90 400,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,90 340,90 370,60 400,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"338\" y=\"55\">MZI-PT</text><text font-size=\"7\" x=\"365\" y=\"74\">θ =1.532</text><text font-size=\"7\" x=\"365\" y=\"80\">ϕ =4.109</text><polyline fill=\"none\" points=\"310,120 340,120 370,150 400,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,150 340,150 370,120 400,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"338\" y=\"115\">MZI-PT</text><text font-size=\"7\" x=\"365\" y=\"134\">θ =1.194</text><text font-size=\"7\" x=\"365\" y=\"140\">ϕ =0.974</text><polyline fill=\"none\" points=\"400,30 430,30 460,60 490,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,60 430,60 460,30 490,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"428\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"455\" y=\"44\">θ =3.142</text><text font-size=\"7\" x=\"455\" y=\"50\">ϕ =6.127</text><polyline fill=\"none\" points=\"400,90 430,90 460,120 490,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,120 430,120 460,90 490,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"428\" y=\"85\">MZI-PT</text><text font-size=\"7\" x=\"455\" y=\"104\">θ =0.389</text><text font-size=\"7\" x=\"455\" y=\"110\">ϕ =3.946</text><polyline fill=\"none\" points=\"400,150 430,150 460,180 490,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,180 430,180 460,150 490,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"428\" y=\"145\">MZI-PT</text><text font-size=\"7\" x=\"455\" y=\"164\">θ =0.0</text><text font-size=\"7\" x=\"455\" y=\"170\">ϕ =1.292</text><polyline fill=\"none\" points=\"490,60 520,60 550,90 580,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,90 520,90 550,60 580,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"518\" y=\"55\">MZI-PT</text><text font-size=\"7\" x=\"545\" y=\"74\">θ =0.397</text><text font-size=\"7\" x=\"545\" y=\"80\">ϕ =2.57</text><polyline fill=\"none\" points=\"490,120 520,120 550,150 580,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,150 520,150 550,120 580,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"518\" y=\"115\">MZI-PT</text><text font-size=\"7\" x=\"545\" y=\"134\">θ =3.142</text><text font-size=\"7\" x=\"545\" y=\"140\">ϕ =0.779</text><polyline fill=\"none\" points=\"490,30 580,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"532.5\" y=\"25\" /><text font-size=\"9\" x=\"530\" y=\"20\">PS</text><text font-size=\"7\" x=\"545\" y=\"20\">θ =0.326</text><polyline fill=\"none\" points=\"580,60 670,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"55\" /><text font-size=\"9\" x=\"620\" y=\"50\">PS</text><text font-size=\"7\" x=\"635\" y=\"50\">θ =5.411</text><polyline fill=\"none\" points=\"580,90 670,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"85\" /><text font-size=\"9\" x=\"620\" y=\"80\">PS</text><text font-size=\"7\" x=\"635\" y=\"80\">θ =5.093</text><polyline fill=\"none\" points=\"580,120 670,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"115\" /><text font-size=\"9\" x=\"620\" y=\"110\">PS</text><text font-size=\"7\" x=\"635\" y=\"110\">θ =2.941</text><polyline fill=\"none\" points=\"580,150 670,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"145\" /><text font-size=\"9\" x=\"620\" y=\"140\">PS</text><text font-size=\"7\" x=\"635\" y=\"140\">θ =6.083</text><polyline fill=\"none\" points=\"490,180 580,180\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"532.5\" y=\"175\" /><text font-size=\"9\" x=\"530\" y=\"170\">PS</text><text font-size=\"7\" x=\"545\" y=\"170\">θ =6.083</text><polyline fill=\"none\" points=\"130,30 220,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,180 220,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,30 400,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,180 400,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,30 670,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,180 670,180\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text><text font-size=\"12\" x=\"25\" y=\"90\">2</text><text font-size=\"12\" x=\"25\" y=\"120\">3</text><text font-size=\"12\" x=\"25\" y=\"150\">4</text><text font-size=\"12\" x=\"25\" y=\"180\">5</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd2f8a53f0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用clements架构实现cnot门\n",
    "u6x6 = np.array([[1, 0, 1, -1, 0, 0],\n",
    "                 [0, 1, 0 ,0,  0, np.sqrt(2)],\n",
    "                 [1,  0, 0, 1, 1, 0],\n",
    "                 [-1, 0, 1, 0, 1, 0],\n",
    "                 [0,  0, 1, 1, -1,0],\n",
    "                 [0, np.sqrt(2), 0,0,0,-1]])/np.sqrt(3)\n",
    "# 将酉矩阵分解成clements对应的参数\n",
    "UD = dq.photonic.decompose.UnitaryDecomposer(u6x6)\n",
    "mzi_info = UD.decomp()\n",
    "# 构造clements线路实现cnot门 \n",
    "clements = dq.photonic.ansatz.Clements(nmode=6, init_state=[1,0,1,0,0,0], cutoff=3)\n",
    "data = clements.dict2data(mzi_info[2]) # encoding the 6x6 data\n",
    "re = clements(data = data)\n",
    "#线路可视化\n",
    "clements.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddec25c7",
   "metadata": {},
   "source": [
    "# 基于高斯后端构建光量子线路"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fd48bd",
   "metadata": {},
   "source": [
    "##  构建连续变量光量子线路"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f778544",
   "metadata": {},
   "source": [
    "基于高斯后端的光量子线路演化的量子态是高斯态， 高斯态的概念来源于连续变量光量子计算， 它对应的wigner函数为多元高斯分布，比如相干态。\n",
    "这里用正交分量X，P的协方差矩阵和平均值来刻画连续变量中的量子态，比如真空态对应的协方差矩阵为单位阵，对应的平均值都为0，\n",
    "通过测量得到的物理量是两个正交分量X，P的值，它们对应的分布满足边缘分布。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0a2b5a",
   "metadata": {},
   "source": [
    "这里构建2模线路，初始量子态为真空高斯态， 经过3种高斯门演化之后得到另一个高斯态。S 表示单模真空压缩门， D表示位移门， BS是可调分束器。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c622b1df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:27:44.775013Z",
     "start_time": "2024-06-20T02:27:44.755777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"1.8181818181818181cm\" version=\"1.1\" width=\"9.3cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"25\" /><text font-size=\"9\" x=\"83\" y=\"20\">S</text><text font-size=\"7\" x=\"95\" y=\"18\">r =1.0</text><text font-size=\"7\" x=\"95\" y=\"24\">θ =0.0</text><polyline fill=\"none\" points=\"40,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"55\" /><text font-size=\"9\" x=\"83\" y=\"50\">S</text><text font-size=\"7\" x=\"95\" y=\"48\">r =1.0</text><text font-size=\"7\" x=\"95\" y=\"54\">θ =0.0</text><polyline fill=\"none\" points=\"130,30 220,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"green\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"172.5\" y=\"25\" /><text font-size=\"9\" x=\"173\" y=\"20\">D</text><text font-size=\"7\" x=\"185\" y=\"18\">r =1.0</text><text font-size=\"7\" x=\"185\" y=\"24\">θ =0.0</text><polyline fill=\"none\" points=\"130,60 220,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"green\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"172.5\" y=\"55\" /><text font-size=\"9\" x=\"173\" y=\"50\">D</text><text font-size=\"7\" x=\"185\" y=\"48\">r =1.0</text><text font-size=\"7\" x=\"185\" y=\"54\">θ =0.0</text><polyline fill=\"none\" points=\"220,30 250,30 280,60 310,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,60 250,60 280,30 310,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"260\" y=\"25\">BS</text><text font-size=\"7\" x=\"275\" y=\"44\">θ =0.785</text><text font-size=\"7\" x=\"275\" y=\"50\">ϕ =0.785</text><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd318229b0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ = dq.photonic.QumodeCircuit(nmode=2, init_state='vac', cutoff=3, backend='gaussian')\n",
    "circ.s(wires=[0], inputs=[1,0])\n",
    "circ.s(wires=[1], inputs=[1,0])\n",
    "circ.d(wires=[0], inputs=[1,0])\n",
    "circ.d(wires=[1], inputs=[1,0])\n",
    "circ.bs(wires=[0,1], inputs=[np.pi/4,np.pi/4])\n",
    "#线路可视化 \n",
    "circ.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "506725e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:27:45.425719Z",
     "start_time": "2024-06-20T02:27:45.411095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([[[ 1.9488,  0.0000,  1.8134, -2.5646],\n",
      "         [ 0.0000,  1.9488, -2.5646, -1.8134],\n",
      "         [ 1.8134, -2.5646,  5.5756,  0.0000],\n",
      "         [-2.5646, -1.8134,  0.0000,  5.5756]]]), tensor([[[0.4142],\n",
      "         [2.4142],\n",
      "         [1.0000],\n",
      "         [1.0000]]])]\n"
     ]
    }
   ],
   "source": [
    "state = circ() # 线路演化后得到的高斯态用协方差矩阵和平均值刻画\n",
    "print(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6d8f6d09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:27:46.142263Z",
     "start_time": "2024-06-20T02:27:46.131191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1024, 4])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measure_re = circ.measure_homodyne(shots = 1024) #对正交分量进行测量， 这里的每个模对应一组正交分量X，P。\n",
    "measure_re.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4256d9fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:27:46.764448Z",
     "start_time": "2024-06-20T02:27:46.755232Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1.6740, 3.0882]), tensor([6.7859, 6.9773]))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photon_mean, photon_var = circ.photon_number_mean_var()# 计算每个模的平均光子数和光子数方差\n",
    "photon_mean, photon_var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202fcb26",
   "metadata": {},
   "source": [
    "## 高斯玻色采样"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9f9eca",
   "metadata": {},
   "source": [
    "基于压缩门和线性光学器件可以构建高斯玻色采样线路"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d3653be3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-20T02:27:50.000145Z",
     "start_time": "2024-06-20T02:27:48.227568Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "chain 1: 100%|\u001b[32m█████████████████████████████\u001b[0m| 999/999 [00:00<00:00, 13517.57it/s]\u001b[0m\n",
      "chain 2: 100%|\u001b[32m█████████████████████████████\u001b[0m| 999/999 [00:00<00:00, 19365.93it/s]\u001b[0m\n",
      "chain 3: 100%|\u001b[32m█████████████████████████████\u001b[0m| 999/999 [00:00<00:00, 20435.07it/s]\u001b[0m\n",
      "chain 4: 100%|\u001b[32m█████████████████████████████\u001b[0m| 999/999 [00:00<00:00, 31056.94it/s]\u001b[0m\n",
      "chain 5: 100%|\u001b[32m█████████████████████████████\u001b[0m| 999/999 [00:00<00:00, 19404.32it/s]\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"5.454545454545454cm\" version=\"1.1\" width=\"22.8cm\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:ev=\"http://www.w3.org/2001/xml-events\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><defs /><polyline fill=\"none\" points=\"40,30 130,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"25\" /><text font-size=\"9\" x=\"83\" y=\"20\">S</text><text font-size=\"7\" x=\"95\" y=\"18\">r =-0.107</text><text font-size=\"7\" x=\"95\" y=\"24\">θ =0.0</text><polyline fill=\"none\" points=\"40,60 130,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"55\" /><text font-size=\"9\" x=\"83\" y=\"50\">S</text><text font-size=\"7\" x=\"95\" y=\"48\">r =-0.235</text><text font-size=\"7\" x=\"95\" y=\"54\">θ =0.0</text><polyline fill=\"none\" points=\"40,90 130,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"85\" /><text font-size=\"9\" x=\"83\" y=\"80\">S</text><text font-size=\"7\" x=\"95\" y=\"78\">r =-0.455</text><text font-size=\"7\" x=\"95\" y=\"84\">θ =0.0</text><polyline fill=\"none\" points=\"40,120 130,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"115\" /><text font-size=\"9\" x=\"83\" y=\"110\">S</text><text font-size=\"7\" x=\"95\" y=\"108\">r =-0.593</text><text font-size=\"7\" x=\"95\" y=\"114\">θ =0.0</text><polyline fill=\"none\" points=\"40,150 130,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"145\" /><text font-size=\"9\" x=\"83\" y=\"140\">S</text><text font-size=\"7\" x=\"95\" y=\"138\">r =-0.818</text><text font-size=\"7\" x=\"95\" y=\"144\">θ =0.0</text><polyline fill=\"none\" points=\"40,180 130,180\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"royalblue\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"10\" x=\"82.5\" y=\"175\" /><text font-size=\"9\" x=\"83\" y=\"170\">S</text><text font-size=\"7\" x=\"95\" y=\"168\">r =-1.495</text><text font-size=\"7\" x=\"95\" y=\"174\">θ =0.0</text><polyline fill=\"none\" points=\"130,30 160,30 190,60 220,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,60 160,60 190,30 220,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"158\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"185\" y=\"44\">θ =1.846</text><text font-size=\"7\" x=\"185\" y=\"50\">ϕ =1.571</text><polyline fill=\"none\" points=\"130,90 160,90 190,120 220,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,120 160,120 190,90 220,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"158\" y=\"85\">MZI-PT</text><text font-size=\"7\" x=\"185\" y=\"104\">θ =2.877</text><text font-size=\"7\" x=\"185\" y=\"110\">ϕ =4.712</text><polyline fill=\"none\" points=\"130,150 160,150 190,180 220,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"130,180 160,180 190,150 220,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"158\" y=\"145\">MZI-PT</text><text font-size=\"7\" x=\"185\" y=\"164\">θ =2.111</text><text font-size=\"7\" x=\"185\" y=\"170\">ϕ =4.712</text><polyline fill=\"none\" points=\"220,60 250,60 280,90 310,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,90 250,90 280,60 310,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"248\" y=\"55\">MZI-PT</text><text font-size=\"7\" x=\"275\" y=\"74\">θ =0.758</text><text font-size=\"7\" x=\"275\" y=\"80\">ϕ =0.515</text><polyline fill=\"none\" points=\"220,120 250,120 280,150 310,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,150 250,150 280,120 310,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"248\" y=\"115\">MZI-PT</text><text font-size=\"7\" x=\"275\" y=\"134\">θ =1.077</text><text font-size=\"7\" x=\"275\" y=\"140\">ϕ =4.329</text><polyline fill=\"none\" points=\"310,30 340,30 370,60 400,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,60 340,60 370,30 400,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"338\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"365\" y=\"44\">θ =1.644</text><text font-size=\"7\" x=\"365\" y=\"50\">ϕ =5.607</text><polyline fill=\"none\" points=\"310,90 340,90 370,120 400,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,120 340,120 370,90 400,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"338\" y=\"85\">MZI-PT</text><text font-size=\"7\" x=\"365\" y=\"104\">θ =1.335</text><text font-size=\"7\" x=\"365\" y=\"110\">ϕ =1.348</text><polyline fill=\"none\" points=\"310,150 340,150 370,180 400,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"310,180 340,180 370,150 400,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"338\" y=\"145\">MZI-PT</text><text font-size=\"7\" x=\"365\" y=\"164\">θ =0.022</text><text font-size=\"7\" x=\"365\" y=\"170\">ϕ =4.174</text><polyline fill=\"none\" points=\"400,60 430,60 460,90 490,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,90 430,90 460,60 490,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"428\" y=\"55\">MZI-PT</text><text font-size=\"7\" x=\"455\" y=\"74\">θ =0.874</text><text font-size=\"7\" x=\"455\" y=\"80\">ϕ =1.193</text><polyline fill=\"none\" points=\"400,120 430,120 460,150 490,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,150 430,150 460,120 490,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"428\" y=\"115\">MZI-PT</text><text font-size=\"7\" x=\"455\" y=\"134\">θ =0.778</text><text font-size=\"7\" x=\"455\" y=\"140\">ϕ =0.376</text><polyline fill=\"none\" points=\"490,30 520,30 550,60 580,60\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,60 520,60 550,30 580,30\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"518\" y=\"25\">MZI-PT</text><text font-size=\"7\" x=\"545\" y=\"44\">θ =0.365</text><text font-size=\"7\" x=\"545\" y=\"50\">ϕ =0.059</text><polyline fill=\"none\" points=\"490,90 520,90 550,120 580,120\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,120 520,120 550,90 580,90\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"518\" y=\"85\">MZI-PT</text><text font-size=\"7\" x=\"545\" y=\"104\">θ =0.905</text><text font-size=\"7\" x=\"545\" y=\"110\">ϕ =0.328</text><polyline fill=\"none\" points=\"490,150 520,150 550,180 580,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"490,180 520,180 550,150 580,150\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"518\" y=\"145\">MZI-PT</text><text font-size=\"7\" x=\"545\" y=\"164\">θ =0.603</text><text font-size=\"7\" x=\"545\" y=\"170\">ϕ =1.182</text><polyline fill=\"none\" points=\"580,60 610,60 640,90 670,90\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,90 610,90 640,60 670,60\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"608\" y=\"55\">MZI-PT</text><text font-size=\"7\" x=\"635\" y=\"74\">θ =0.632</text><text font-size=\"7\" x=\"635\" y=\"80\">ϕ =3.74</text><polyline fill=\"none\" points=\"580,120 610,120 640,150 670,150\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"580,150 610,150 640,120 670,120\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"9\" x=\"608\" y=\"115\">MZI-PT</text><text font-size=\"7\" x=\"635\" y=\"134\">θ =0.843</text><text font-size=\"7\" x=\"635\" y=\"140\">ϕ =1.031</text><polyline fill=\"none\" points=\"580,30 670,30\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"25\" /><text font-size=\"9\" x=\"620\" y=\"20\">PS</text><text font-size=\"7\" x=\"635\" y=\"20\">θ =4.973</text><polyline fill=\"none\" points=\"670,60 760,60\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"712.5\" y=\"55\" /><text font-size=\"9\" x=\"710\" y=\"50\">PS</text><text font-size=\"7\" x=\"725\" y=\"50\">θ =2.488</text><polyline fill=\"none\" points=\"670,90 760,90\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"712.5\" y=\"85\" /><text font-size=\"9\" x=\"710\" y=\"80\">PS</text><text font-size=\"7\" x=\"725\" y=\"80\">θ =5.63</text><polyline fill=\"none\" points=\"670,120 760,120\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"712.5\" y=\"115\" /><text font-size=\"9\" x=\"710\" y=\"110\">PS</text><text font-size=\"7\" x=\"725\" y=\"110\">θ =4.494</text><polyline fill=\"none\" points=\"670,150 760,150\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"712.5\" y=\"145\" /><text font-size=\"9\" x=\"710\" y=\"140\">PS</text><text font-size=\"7\" x=\"725\" y=\"140\">θ =4.494</text><polyline fill=\"none\" points=\"580,180 670,180\" stroke=\"black\" stroke-width=\"2\" /><rect fill=\"teal\" height=\"12\" rx=\"0\" ry=\"0\" stroke=\"black\" stroke-width=\"1.5\" width=\"6\" x=\"622.5\" y=\"175\" /><text font-size=\"9\" x=\"620\" y=\"170\">PS</text><text font-size=\"7\" x=\"635\" y=\"170\">θ =0.203</text><polyline fill=\"none\" points=\"220,30 310,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"220,180 310,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,30 490,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"400,180 490,180\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"670,30 760,30\" stroke=\"black\" stroke-width=\"2\" /><polyline fill=\"none\" points=\"670,180 760,180\" stroke=\"black\" stroke-width=\"2\" /><text font-size=\"12\" x=\"25\" y=\"30\">0</text><text font-size=\"12\" x=\"25\" y=\"60\">1</text><text font-size=\"12\" x=\"25\" y=\"90\">2</text><text font-size=\"12\" x=\"25\" y=\"120\">3</text><text font-size=\"12\" x=\"25\" y=\"150\">4</text><text font-size=\"12\" x=\"25\" y=\"180\">5</text></svg>"
      ],
      "text/plain": [
       "<svgwrite.drawing.Drawing at 0x1dd33f79600>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 采用6个节点的图演示简单的高斯玻色采样\n",
    "a = np.array([[0., 1., 1., 0., 0., 0.],\n",
    "       [1., 0., 0., 1., 0., 1.],\n",
    "       [1., 0., 0., 0., 0., 0.],\n",
    "       [0., 1., 0., 0., 1., 1.],\n",
    "       [0., 0., 0., 1., 0., 0.],\n",
    "       [0., 1., 0., 1., 0., 0.]])\n",
    "gbs = dq.photonic.ansatz.GBS_Graph(adj_mat=torch.tensor(a), cutoff=2)\n",
    "state = gbs()\n",
    "sample = gbs.measure(shots=5000)\n",
    "gbs.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9376d2ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T09:23:08.254277Z",
     "start_time": "2024-06-19T09:23:08.248450Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{|001010>: 1,\n",
       " |010001>: 301,\n",
       " |000110>: 317,\n",
       " |101000>: 385,\n",
       " |010100>: 346,\n",
       " |000000>: 2336,\n",
       " |000101>: 431,\n",
       " |110101>: 60,\n",
       " |110000>: 413,\n",
       " |110110>: 61,\n",
       " |101101>: 42,\n",
       " |010111>: 65,\n",
       " |111001>: 73,\n",
       " |101110>: 83,\n",
       " |111100>: 76,\n",
       " |110011>: 2,\n",
       " |111111>: 8}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6100a750",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-18T06:09:55.195949Z",
     "start_time": "2023-12-18T06:09:55.191952Z"
    }
   },
   "source": [
    "# 随机电路测试 (与perceval 比较)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ede25026",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-18T06:15:16.714067Z",
     "start_time": "2023-12-18T06:15:16.710454Z"
    }
   },
   "outputs": [],
   "source": [
    "import perceval as pcvl\n",
    "import perceval.components as comp\n",
    "from perceval.components import BS\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bb96c7ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-18T06:19:36.573926Z",
     "start_time": "2023-12-18T06:19:31.991977Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_id: 0 | max_difference: 2.2838e-06 | T1: 0.02s | T2: 0.15s | nm: 6 | nd: 79\n",
      "test_id: 1 | max_difference: 7.8976e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 89\n",
      "test_id: 2 | max_difference: 8.9407e-08 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 37\n",
      "test_id: 3 | max_difference: 8.8279e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 73\n",
      "test_id: 4 | max_difference: 4.0395e-06 | T1: 0.03s | T2: 0.20s | nm: 6 | nd: 84\n",
      "test_id: 5 | max_difference: 1.2288e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 10\n",
      "test_id: 6 | max_difference: 1.7270e-06 | T1: 0.03s | T2: 0.11s | nm: 6 | nd: 56\n",
      "test_id: 7 | max_difference: 3.6652e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 13\n",
      "test_id: 8 | max_difference: 1.9332e-06 | T1: 0.02s | T2: 0.09s | nm: 6 | nd: 15\n",
      "test_id: 9 | max_difference: 1.3448e-06 | T1: 0.01s | T2: 0.02s | nm: 5 | nd: 53\n",
      "test_id: 10 | max_difference: 7.8398e-07 | T1: 0.00s | T2: 0.01s | nm: 3 | nd: 75\n",
      "test_id: 11 | max_difference: 6.6390e-07 | T1: 0.00s | T2: 0.04s | nm: 5 | nd: 16\n",
      "test_id: 12 | max_difference: 5.8704e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 83\n",
      "test_id: 13 | max_difference: 2.6987e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 71\n",
      "test_id: 14 | max_difference: 4.2068e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 36\n",
      "test_id: 15 | max_difference: 4.2944e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 70\n",
      "test_id: 16 | max_difference: 2.0916e-06 | T1: 0.02s | T2: 0.13s | nm: 6 | nd: 49\n",
      "test_id: 17 | max_difference: 1.3042e-06 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 59\n",
      "test_id: 18 | max_difference: 3.6991e-07 | T1: 0.00s | T2: 0.01s | nm: 3 | nd: 75\n",
      "test_id: 19 | max_difference: 5.5421e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 29\n",
      "test_id: 20 | max_difference: 8.2011e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 56\n",
      "test_id: 21 | max_difference: 8.7041e-07 | T1: 0.00s | T2: 0.01s | nm: 3 | nd: 99\n",
      "test_id: 22 | max_difference: 9.1279e-07 | T1: 0.03s | T2: 0.14s | nm: 6 | nd: 12\n",
      "test_id: 23 | max_difference: 4.3393e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 72\n",
      "test_id: 24 | max_difference: 5.8419e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 13\n",
      "test_id: 25 | max_difference: 7.2151e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 97\n",
      "test_id: 26 | max_difference: 5.3727e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 83\n",
      "test_id: 27 | max_difference: 8.8782e-07 | T1: 0.03s | T2: 0.15s | nm: 6 | nd: 87\n",
      "test_id: 28 | max_difference: 2.5908e-06 | T1: 0.02s | T2: 0.11s | nm: 6 | nd: 62\n",
      "test_id: 29 | max_difference: 5.2133e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 78\n",
      "test_id: 30 | max_difference: 8.8713e-07 | T1: 0.00s | T2: 0.01s | nm: 3 | nd: 68\n",
      "test_id: 31 | max_difference: 6.5835e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 43\n",
      "test_id: 32 | max_difference: 7.8606e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 56\n",
      "test_id: 33 | max_difference: 5.9902e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 29\n",
      "test_id: 34 | max_difference: 9.4544e-07 | T1: 0.03s | T2: 0.11s | nm: 6 | nd: 14\n",
      "test_id: 35 | max_difference: 4.5449e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 83\n",
      "test_id: 36 | max_difference: 8.1784e-07 | T1: 0.00s | T2: 0.03s | nm: 4 | nd: 82\n",
      "test_id: 37 | max_difference: 1.0492e-06 | T1: 0.04s | T2: 0.09s | nm: 6 | nd: 84\n",
      "test_id: 38 | max_difference: 9.3542e-07 | T1: 0.02s | T2: 0.11s | nm: 6 | nd: 66\n",
      "test_id: 39 | max_difference: 3.5763e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 19\n",
      "test_id: 40 | max_difference: 4.0047e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 60\n",
      "test_id: 41 | max_difference: 1.5680e-06 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 12\n",
      "test_id: 42 | max_difference: 1.5840e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 39\n",
      "test_id: 43 | max_difference: 4.5099e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 11\n",
      "test_id: 44 | max_difference: 7.7987e-07 | T1: 0.03s | T2: 0.10s | nm: 6 | nd: 27\n",
      "test_id: 45 | max_difference: 5.0333e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 61\n",
      "test_id: 46 | max_difference: 4.3393e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 57\n",
      "test_id: 47 | max_difference: 9.9202e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 90\n",
      "test_id: 48 | max_difference: 2.4027e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 23\n",
      "test_id: 49 | max_difference: 2.9324e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 66\n",
      "test_id: 50 | max_difference: 1.5471e-06 | T1: 0.01s | T2: 0.04s | nm: 5 | nd: 56\n",
      "test_id: 51 | max_difference: 4.3316e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 53\n",
      "test_id: 52 | max_difference: 7.3738e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 95\n",
      "test_id: 53 | max_difference: 9.3414e-07 | T1: 0.03s | T2: 0.14s | nm: 6 | nd: 84\n",
      "test_id: 54 | max_difference: 8.6888e-07 | T1: 0.00s | T2: 0.01s | nm: 3 | nd: 89\n",
      "test_id: 55 | max_difference: 9.5577e-07 | T1: 0.02s | T2: 0.10s | nm: 6 | nd: 99\n",
      "test_id: 56 | max_difference: 5.2983e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 33\n",
      "test_id: 57 | max_difference: 9.7219e-07 | T1: 0.03s | T2: 0.11s | nm: 6 | nd: 49\n",
      "test_id: 58 | max_difference: 6.7649e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 21\n",
      "test_id: 59 | max_difference: 6.7448e-06 | T1: 0.03s | T2: 0.12s | nm: 6 | nd: 97\n",
      "test_id: 60 | max_difference: 4.9151e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 51\n",
      "test_id: 61 | max_difference: 6.5362e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 27\n",
      "test_id: 62 | max_difference: 7.4043e-07 | T1: 0.02s | T2: 0.10s | nm: 6 | nd: 16\n",
      "test_id: 63 | max_difference: 3.9888e-06 | T1: 0.04s | T2: 0.11s | nm: 6 | nd: 96\n",
      "test_id: 64 | max_difference: 6.2126e-07 | T1: 0.00s | T2: 0.01s | nm: 3 | nd: 83\n",
      "test_id: 65 | max_difference: 1.7064e-06 | T1: 0.02s | T2: 0.14s | nm: 6 | nd: 21\n",
      "test_id: 66 | max_difference: 6.9574e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 84\n",
      "test_id: 67 | max_difference: 4.0536e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 19\n",
      "test_id: 68 | max_difference: 1.2320e-06 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 79\n",
      "test_id: 69 | max_difference: 1.4630e-06 | T1: 0.03s | T2: 0.11s | nm: 6 | nd: 61\n",
      "test_id: 70 | max_difference: 1.7625e-06 | T1: 0.02s | T2: 0.10s | nm: 6 | nd: 43\n",
      "test_id: 71 | max_difference: 1.6233e-06 | T1: 0.01s | T2: 0.04s | nm: 5 | nd: 68\n",
      "test_id: 72 | max_difference: 2.1170e-06 | T1: 0.01s | T2: 0.04s | nm: 5 | nd: 55\n",
      "test_id: 73 | max_difference: 2.0546e-06 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 38\n",
      "test_id: 74 | max_difference: 9.1724e-07 | T1: 0.01s | T2: 0.04s | nm: 5 | nd: 45\n",
      "test_id: 75 | max_difference: 1.0393e-06 | T1: 0.00s | T2: 0.03s | nm: 5 | nd: 25\n",
      "test_id: 76 | max_difference: 6.2247e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 71\n",
      "test_id: 77 | max_difference: 1.5668e-06 | T1: 0.01s | T2: 0.05s | nm: 5 | nd: 59\n",
      "test_id: 78 | max_difference: 5.2726e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 20\n",
      "test_id: 79 | max_difference: 8.9072e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 47\n",
      "test_id: 80 | max_difference: 4.1830e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 11\n",
      "test_id: 81 | max_difference: 1.4605e-06 | T1: 0.02s | T2: 0.10s | nm: 6 | nd: 23\n",
      "test_id: 82 | max_difference: 8.3659e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 91\n",
      "test_id: 83 | max_difference: 1.4706e-06 | T1: 0.03s | T2: 0.12s | nm: 6 | nd: 47\n",
      "test_id: 84 | max_difference: 4.3521e-07 | T1: 0.00s | T2: 0.02s | nm: 4 | nd: 48\n",
      "test_id: 85 | max_difference: 1.0637e-06 | T1: 0.03s | T2: 0.12s | nm: 6 | nd: 63\n",
      "test_id: 86 | max_difference: 2.7618e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 29\n",
      "test_id: 87 | max_difference: 4.5099e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 15\n",
      "test_id: 88 | max_difference: 5.8729e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 37\n",
      "test_id: 89 | max_difference: 7.6911e-07 | T1: 0.00s | T2: 0.01s | nm: 2 | nd: 92\n",
      "test_id: 90 | max_difference: 8.6770e-07 | T1: 0.03s | T2: 0.12s | nm: 6 | nd: 97\n",
      "test_id: 91 | max_difference: 7.9638e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 44\n",
      "test_id: 92 | max_difference: 8.4466e-07 | T1: 0.01s | T2: 0.06s | nm: 5 | nd: 46\n",
      "test_id: 93 | max_difference: 5.7483e-07 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 27\n",
      "test_id: 94 | max_difference: 1.2134e-06 | T1: 0.01s | T2: 0.06s | nm: 5 | nd: 98\n",
      "test_id: 95 | max_difference: 4.9151e-07 | T1: 0.00s | T2: 0.00s | nm: 2 | nd: 23\n",
      "test_id: 96 | max_difference: 1.3953e-06 | T1: 0.01s | T2: 0.03s | nm: 5 | nd: 79\n",
      "test_id: 97 | max_difference: 4.3603e-07 | T1: 0.00s | T2: 0.00s | nm: 3 | nd: 40\n",
      "test_id: 98 | max_difference: 2.0319e-06 | T1: 0.02s | T2: 0.24s | nm: 6 | nd: 66\n",
      "test_id: 99 | max_difference: 3.6878e-07 | T1: 0.00s | T2: 0.01s | nm: 4 | nd: 31\n"
     ]
    }
   ],
   "source": [
    "## for state list\n",
    "for test_id in range(100): # run 100 random circuit\n",
    "    nmode = np.random.randint(2,7)\n",
    "    ndevice = np.random.randint(10,100)\n",
    "    ini_state_pre = np.random.randn(nmode)\n",
    "    ini_state_pre = ini_state_pre-ini_state_pre.min()\n",
    "    ini_state_pre = np.round(ini_state_pre/ini_state_pre.sum()*nmode)\n",
    "    ini_state_pre[ini_state_pre<0] = 0\n",
    "    if ini_state_pre.sum()!=nmode:\n",
    "        ini_state_pre[ini_state_pre.argmax()] += nmode - ini_state_pre.sum()\n",
    "    ini_state = [int(i) for i in ini_state_pre]\n",
    "    ini_state_ts = [(1, ini_state)]\n",
    "    assert np.sum(ini_state_pre)==nmode\n",
    "\n",
    "    test_gate = (pcvl.Circuit (nmode, name =\" test1 \"))\n",
    "\n",
    "    dq_gate = dq.QumodeCircuit(nmode=nmode, init_state=ini_state, name=\"test\", cutoff = sum(ini_state)+1, basis = True)\n",
    "\n",
    "    encode = True\n",
    "\n",
    "    for i in range(ndevice): ## take the random circuit\n",
    "        j = np.random.uniform(-1,1)\n",
    "        if j>0:\n",
    "            temp_1 = int(np.random.choice(np.arange(nmode)))\n",
    "            angle_1 = np.random.uniform(0,2*np.pi)\n",
    "            test_gate.add((temp_1),comp.PS(angle_1))\n",
    "            dq_gate.ps([temp_1],angle_1,encode=encode)\n",
    "        else:\n",
    "            k = int(np.random.choice(np.arange(nmode-1)))\n",
    "            angle_2 = np.random.uniform(0,2*np.pi)\n",
    "            test_gate.add((k,k+1), BS.Rx(angle_2))\n",
    "            dq_gate.bs_theta([k, k+1],angle_2/2,encode=encode)\n",
    "\n",
    "\n",
    "    t1 = time.perf_counter()\n",
    "    backend = pcvl.BackendFactory().get_backend(\"Naive\")\n",
    "    backend.set_circuit(test_gate)\n",
    "    input_state=pcvl.BasicState(ini_state)\n",
    "    backend.set_input_state(input_state)\n",
    "    re1 = backend.evolve()\n",
    "    t2 = time.perf_counter()\n",
    "    T1 = t2-t1\n",
    "\n",
    "    t3 = time.perf_counter()\n",
    "    re2 = dq_gate()\n",
    "    t4 = time.perf_counter()\n",
    "    T2 = t4-t3\n",
    "\n",
    "    ## calculating the difference for two simu approach\n",
    "    max_error = -1.0\n",
    "    for key in re1.keys():\n",
    "        key2 = list(key)\n",
    "        key3 = dq.photonic.FockState(key2)\n",
    "        tmp_error =  abs(re2[(key3)]-re1[(key)])\n",
    "#         tmp_error =  abs(re2[tuple(key2)]-re1[(key)])\n",
    "        if tmp_error > max_error:\n",
    "            max_error = tmp_error\n",
    "    if max_error>1e-3:\n",
    "        raise Exception(\"check this case\")\n",
    "    print(f\"test_id: {test_id} | max_difference: {'%.4e' %max_error} | T1: {'%.2f' %T1}s | T2: {'%.2f' %T2}s | nm: {nmode} | nd: {ndevice}\")"
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